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Project Database
Project Reference: ITP/052/21LP
Project Title: Graph and Cluster Based Text Analytics Technology for Discovering Meaningful Relationship Patterns from Economic and Innovation Development Corpuses
Hosting Institution: LSCM R&D Centre (LSCM)
Abstract: This seed project aims to turn a corpus of qualitative text information into quantifiable
contextual relationships between diverse content structured in graph and clusters. Such
a quantified representation enables the use of analytic techniques over qualitative text
corpuses to gain different but possibly broader insights beyond the usual operational
environments monitored by underlying quantitative transactional and IoT data. Corpuses
collected from communication and reporting means provide a wider context covering
areas like public health, economic, politics, and social interest from around the world.
The R&D work of the project attempts to explore the use of graph and cluster analytics to
structure an information space of text content into graph and cluster relationships, which
are quantifiable linkages between text content. Local context in individual text passages
will be leveraged to build concept graph, or term-to-term relationships, from the
underlying domain-specific or application-specific corpus. In addition, content similarity
among text passages in a corpus will be utilized to form contextual clusters. The textual
information space in graph and clusters becomes a set of structured contextual
relationships to support computational analytics for unveiling insights, monitoring statuses
from multiple perspectives, or performing qualitative scenario analysis. The project also
attempts to develop analytical mechanisms for navigating and exploring the structured
information space to assist users to get insights into their areas of interest. Context-free
navigation and summarization mechanisms will be developed to gather relevant content
summarizing in multiple perspectives at different levels of abstraction as insights to
application or problem-specific user issues. Two experiments will be conducted to
demonstrate how textual information is turned into corresponding structured information
space and how the structured space is navigated to get insights for corresponding
analytics requests. An experiment will use an economic development related dataset
from Invest Hong Kong. Another one will use an innovation development dataset from
ITF Project Database.
Project Coordinator: Dr Dorbin Ng
Approved Funding Amount: HK$ 2.7 M
Project Period: 1 Mar 2022 - 30 Mar 2023
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